And here’s an experimental podcast-style of paper summary, generated via Notebook LM directed by me! Link: notebooklm.google.com/notebook/9c8...

Our paper is now accepted at Neural Networks! This work builds on our previous threads in X, updated with deeper analyses. We revisit brain-to-image reconstruction using NSD + diffusion models—and ask: do they really reconstruct what we perceive? Paper: doi.org/10.1016/j.ne... 🧵1/12

Yukiyasu Kamitani, Misato Tanaka, Ken Shirakawa Visual Image Reconstruction from Brain Activity via Latent Representation https://arxiv.org/abs/2505.08429

One big issue with some of the previous claims are that NSD, the massive 7T fMRI dataset of 1000s of images, might not be the right dataset to test these hypotheses. The reason is that it is built on MSCoCo and has too high similarity between training and test. arxiv.org/abs/2405.10078 16/n

I’m currently concerned about what the brain’s encoding model predicts. Given that the target brain state is collected under naturalistic condition and the inputs of encoding model derived from a deep neural network, I am not sure what the predictions are actually represent.